BACKGROUND: Severe primary (degenerative) mitral regurgitation (MR) is repaired with durable results when simple single-scallop disease is addressed. The midterm quality outcomes of minimally invasive repair for complex disease are unknown, however.
We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural netwo...
PURPOSE: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable f...
BACKGROUND: The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, convent...
PURPOSE: Limited data are available regarding the oncologic efficacy of pelvic lymph node dissection (PLND) performed during robotic-assisted laparoscopic prostatectomy (RALP) for prostate cancer. We aimed to determine the frequency of pelvic lymph n...
Biological outcomes are governed by multiple genetic and environmental factors that act in concert. Determining multifactor interactions is the primary topic of interest in recent genetics studies but presents enormous statistical and mathematical ch...
Annals of the American Thoracic Society
May 1, 2025
Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset...
Studies in health technology and informatics
Apr 8, 2025
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting s...
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these mo...